Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regularized LightGBM× | CatBoost× | Gradient Boosting× | |
|---|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2017 | 2018 | 2001 |
| Autors≠ | Ke, G. et al. (Microsoft Research) | Prokhorenkova, L. et al. (Yandex) | Friedman, J. H. |
| Tips≠ | Regularized gradient boosting ensemble | Gradient boosting on decision trees | Ensemble (sequential boosting of decision trees) |
| Pirmavots≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Citi nosaukumi | LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Saistītās | 5 | 5 | 5 |
| Kopsavilkums≠ | Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or noisy feature sets. | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
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